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Performance evaluation and optimization of long range IoT network using whale optimization algorithm

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Abstract

Apart from supporting the essential requirements of low power consumption in the IoT networks, Long Range (LoRa) technology provides wide-area coverage, and massive scalability at a low cost. The conventional LoRa network follows a single-hop approach, which may lead to greater path loss and rapid battery depletion thereby rendering low coverage and connectivity. In this work, a dual-hop LoRa technology incorporating cooperative communication is presented and a mathematical framework has been formulated in terms of received power and signal-to-noise ratio (SNR). Further, outage probability, spectral efficiency and throughput are computed under the Rayleigh fading channel to explore the proposed LoRa network performance. A critical comparative analysis of the dual-hop LoRa network with the conventional single-hop LoRa network has been done. Evaluation reveals 75%, 62% and 70% improvement in the outage probability, spectral efficiency and throughput respectively by incorporating a cooperative scenario in the LoRa network. In addition, to enhance the performance of the LoRa nodes in both cooperative as well as non-cooperative scenarios, a metaheuristic optimization approach known as the whale optimization algorithm (WOA) is utilized. Based on the mathematical model, an optimization problem is defined whose solution gives the optimal transmission parameters to maximize the received power. The performance metrics evaluated utilizing the optimized solution discovered using WOA demonstrate the improved performance of the LoRa nodes in both the cooperative as well as non-cooperative scenarios.

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References

  1. Lavric, A., Popa, V.: Internet of Things and LoRaTM Low-power wide-area networks: a survey. In: ISSCS 2017—International Symposium on Signals, Circuits and Systems. Institute of Electrical and Electronics Engineers Inc. (2017)

  2. Qadir, Q.M., Rashid, T.A., Al-Salihi, N.K., Ismael, B., Kist, A.A., Zhang, Z.: Low power wide area networks: a survey of enabling technologies, applications and interoperability needs. IEEE Access 6, 77454–77473 (2018). https://doi.org/10.1109/ACCESS.2018.2883151

    Article  Google Scholar 

  3. Raza, U., Kulkarni, P., Sooriyabandara, M.: Low power wide area networks: an overview. IEEE Commun. Surv. Tutor. 19, 855–873 (2017). https://doi.org/10.1109/COMST.2017.2652320

    Article  Google Scholar 

  4. Shanmuga Sundaram, J.P., Du, W., Zhao, Z.: A survey on LoRa networking: research problems, current solutions, and open issues. IEEE Commun. Surv. Tutor. 22, 371–388 (2020). https://doi.org/10.1109/COMST.2019.2949598

    Article  Google Scholar 

  5. Bor, M., Roedig, U.: LoRa transmission parameter selection. In: Proc.—2017 13th Int. Conf. Distrib. Comput. Sens. Syst. DCOSS 2017. 2018-January, 27–34 (2018). https://doi.org/10.1109/DCOSS.2017.10

  6. Verma, S., Gupta, S.H., Sharma, R.: Analysis and optimization of low power wide area IoT network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 98–112. Springer, Deutschland GmbH (2021)

  7. Barrachina-Muñoz, S., Bellalta, B., Adame, T., Bel, A.: Multi-hop communication in the uplink for LPWANs. Comput. Netw. 123, 153–168 (2017). https://doi.org/10.1016/j.comnet.2017.05.020

    Article  Google Scholar 

  8. Lin, J., Jung, H., Chang, Y.J., Jung, J.W., Weitnauer, M.A.: On cooperative transmission range extension in multi-hop wireless ad-hoc and sensor networks: a review. Adhoc Netw. 29, 117–134 (2015)

    Google Scholar 

  9. Pappas, N., Dimitriou, I., Chen, Z.: On the benefits of network-level cooperation in IoT networks with aggregators. Perform. Eval. 147, 102196 (2021). https://doi.org/10.1016/J.PEVA.2021.102196

    Article  Google Scholar 

  10. Gokturk, M.S., Gurbuz, O., Erkip, E.: A cross-layer multi-hop cooperative network architecture for wireless ad hoc networks. Comput. Netw. 57, 4010–4029 (2013). https://doi.org/10.1016/j.comnet.2013.10.003

    Article  Google Scholar 

  11. Uddin, M.F., Assi, C., Ghrayeb, A.: Joint optimal AF relay assignment and power allocation in wireless cooperative networks. Comput. Netw. 58, 58–69 (2014). https://doi.org/10.1016/j.comnet.2013.08.023

    Article  Google Scholar 

  12. Wang, S., Ruby, R., Leung, V.C.M., Yao, Z.: Energy-efficient power allocation for multi-user single-AF-relay underlay cognitive radio networks. Comput. Netw. 103, 115–128 (2016). https://doi.org/10.1016/j.comnet.2016.04.007

    Article  Google Scholar 

  13. Kumar, N., Bhatia, V.: Outage probability and average channel capacity of amplify-and-forward in conventional cooperative communication networks over rayleigh fading channels. Wirel. Pers. Commun. 88, 943–951 (2016). https://doi.org/10.1007/s11277-016-3221-0

    Article  Google Scholar 

  14. Nagar, J., Chaturvedi, S.K., Soh, S.: An analytical framework with border effects to estimate the connectivity performance of finite multihop networks in shadowing environments. Clust. Comput. 25(1), 187–202 (2021). https://doi.org/10.1007/S10586-021-03374-5

    Article  Google Scholar 

  15. Nguyen, T.N., Minh, T.H.Q., Tran, P.T., Vozňák, M.: Energy harvesting over rician fading channel: a performance analysis for half-duplex bidirectional sensor networks under hardware impairments. Sensors 18, 1781 (2018). https://doi.org/10.3390/s18061781

    Article  Google Scholar 

  16. Sah, D.K., Nguyen, T.N., Cengiz, K., Dumba, B., Kumar, V.: Load-balance scheduling for intelligent sensors deployment in industrial internet of things. Clust. Comput. 25(3), 1715–1727 (2021). https://doi.org/10.1007/S10586-021-03316-1

    Article  Google Scholar 

  17. Danish, S.M., Lestas, M., Qureshi, H.K., Zhang, K., Asif, W., Rajarajan, M.: Securing the LoRaWAN join procedure using blockchains. Clust. Comput. 23(3), 2123–2138 (2020). https://doi.org/10.1007/s10586-020-03064-8

    Article  Google Scholar 

  18. Nguyen, T.H., Jung, W.S., Tu, L.T., Chien, T.V., Yoo, D., Ro, S.: Performance analysis and optimization of the coverage probability in dual hop LoRa networks with different fading channels. IEEE Access 8, 107087–107102 (2020). https://doi.org/10.1109/ACCESS.2020.3000600

    Article  Google Scholar 

  19. Aslam, M.S., Khan, A., Atif, A., Hassan, S.A., Mahmood, A., Qureshi, H.K., Gidlund, M.: Exploring multi-hop LoRa for green smart cities. IEEE Netw. 34, 225–231 (2020). https://doi.org/10.1109/MNET.001.1900269

    Article  Google Scholar 

  20. Farooq, M.O.: Clustering-based layering approach for uplink multi-hop communication in LoRa networks. IEEE Netw. Lett. 2, 132–135 (2020). https://doi.org/10.1109/lnet.2020.3003161

    Article  Google Scholar 

  21. Zhu, G., Liao, C.H., Sakdejayont, T., Lai, I.W., Narusue, Y., Morikawa, H.: Improving the capacity of a mesh LoRa network by spreading-factor-based network clustering. IEEE Access 7, 21584–21596 (2019). https://doi.org/10.1109/ACCESS.2019.2898239

    Article  Google Scholar 

  22. Lee, S., Lee, J., Park, H.S., Choi, J.K.: A novel fair and scalable relay control scheme for internet of things in lora-based low-power wide-area networks. IEEE Internet Things J. 8, 5985–6001 (2021). https://doi.org/10.1109/JIOT.2020.3034185

    Article  Google Scholar 

  23. Tran, H.P., Jung, W.S., Yoon, T., Yoo, D.S., Oh, H.: A two-hop real-time LoRa protocol for industrial monitoring and control systems. IEEE Access 8, 126239–126252 (2020). https://doi.org/10.1109/ACCESS.2020.3007985

    Article  Google Scholar 

  24. Farooq, M.O.: Multi-hop communication protocol for LoRa with software-defined networking extension. Internet of Things. 14, 100379 (2021). https://doi.org/10.1016/J.IOT.2021.100379

    Article  Google Scholar 

  25. LoRa Frequency Bands in India|LoRa|LoRaWAN—Ensemble Tech, http://www.ensembletech.in/lora-frequency-bands-india/

  26. Liando, J.C., Gamage, A., Tengourtius, A.W., Li, M.: Known and unknown facts of LoRa: experiences from a large-scale measurement study. ACM Trans. Sens. Netw. 15, 1–35 (2019). https://doi.org/10.1145/3293534

    Article  Google Scholar 

  27. Elshabrawy, T., Robert, J.: Interleaved chirp spreading LoRa-based modulation. IEEE Internet Things J. 6, 3855–3863 (2019). https://doi.org/10.1109/JIOT.2019.2892294

    Article  Google Scholar 

  28. Yousuf, A.M., Rochester, E.M., Ousat, B., Ghaderi, M.: Throughput, coverage and scalability of LoRa LPWAN for Internet of Things. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service, IWQoS 2018. Institute of Electrical and Electronics Engineers Inc. (2019)

  29. Sun, Y., Hu, J., Liu, Y., Tian, Z.: Theoretical analysis and performance testing of LoRa technology. In: Proc.—2017 Int. Conf. Comput. Technol. Electron. Commun. ICCTEC 2017, pp. 686–690 (2017). https://doi.org/10.1109/ICCTEC.2017.00153

  30. Leonardi, L., Battaglia, F., Lo Bello, L.: RT-LoRa: a medium access strategy to support real-time flows over LoRa-based networks for industrial IoT applications. IEEE Internet Things J. 6, 10812–10823 (2019). https://doi.org/10.1109/JIOT.2019.2942776

    Article  Google Scholar 

  31. Ertürk, M.A., Aydın, M.A., Büyükakkaşlar, M.T., Evirgen, H.: A survey on LoRaWAN architecture. Protoc. Technol. Futur. Internet. 11, 216 (2019). https://doi.org/10.3390/fi11100216

    Article  Google Scholar 

  32. Bouguera, T., Diouris, J.F., Chaillout, J.J., Jaouadi, R., Andrieux, G.: Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors 18, 2104 (2018). https://doi.org/10.3390/S18072104

    Article  Google Scholar 

  33. Georgiou, O., Raza, U.: Low power wide area network analysis: can LoRa scale? IEEE Wirel. Commun. Lett. 6, 162–165 (2017). https://doi.org/10.1109/LWC.2016.2647247

    Article  Google Scholar 

  34. Kulkarni, P., Hakim, Q.O.A., Lakas, A.: Experimental evaluation of a campus-deployed IoT network using LoRa. IEEE Sens. J. 20, 2803–2811 (2020). https://doi.org/10.1109/JSEN.2019.2953572

    Article  Google Scholar 

  35. Samb, D., Yu, L.: Performance analysis of amplify and forward cooperative relaying protocol in wireless communication system. Wirel. Pers. Commun. 70, 969–983 (2013). https://doi.org/10.1007/s11277-012-0732-1

    Article  Google Scholar 

  36. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/J.ADVENGSOFT.2016.01.008

    Article  Google Scholar 

  37. Tunc, C., Akar, N.: Markov fluid queue model of an energy harvesting IoT device with adaptive sensing. Perform. Eval. 111, 1–16 (2017). https://doi.org/10.1016/J.PEVA.2017.03.004

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity University, Sector-125, Noida, India

    Gagandeep Kaur & Sindhu Hak Gupta

  2. Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India

    Harleen Kaur

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  1. Gagandeep Kaur
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Kaur, G., Gupta, S.H. & Kaur, H. Performance evaluation and optimization of long range IoT network using whale optimization algorithm. Cluster Comput 26, 3737–3751 (2023). https://doi.org/10.1007/s10586-022-03775-0

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